A Review of Ultrasound Tissue Characterization with Mean Scatterer Spacing

Zhou, Zhuhuang; Wu, Weiwei; Wu, Shuicai; Jia, Kebin; Tsui, Po-Hsiang

doi:10.1177/0161734617692018

Cited by 41 publications

(27 citation statements)

References 83 publications

(165 reference statements)

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“…Comparing our results with other works in the literature is not straightforward, as the signals used to test the MSS estimation differed. Zhou et al (2017) concluded that a comparative analysis of MSS estimators with respect to estimation accuracy, time efficiency and robustness should be carried out.…”

Section: Discussionmentioning

confidence: 99%

“…In 2013, Rubert and Varghese (2013) compared multitaper coherence and single-taper to estimate MSS and concluded that the multi-taper significantly improves over single-taper. Periodicity estimation remains an open problem as can be seen in the recent paper from Zhou et al (2017), that makes a detailed comparison of the 13 most common methods used for MSS estimation, regarding their advantages and limitations. They proposed that a comparative analysis of state-of-the-art MSS estimation methods is, thus, needed in the future, considering their estimation accuracy, time efficiency, and robustness.…”

Section: Introductionmentioning

confidence: 99%

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Mean scatterer space estimation from ultrasound signals combining singular spectral analysis and entropy

Kauati¹,

Pereira

Campos

2018

Res. Biomed. Eng.

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Introduction: Ultrasound (US) is a nonionizing radiation capable of real time imaging at low cost. Its most attractive application is quantitative tissue characterization with the objective of differentiating normal tissues from diseased tissues. In this study, an automated method using singular spectrum analysis (SSA) to estimate the mean scatterer space (MSS) of US signals is proposed. Methods: Entropy was used to determine the optimal number of components for the SSA. Subsequently, this number was compared with the results using a fixed number of components. A method based on the spectrum of the original signal was also used for comparison. The method was evaluated by using 24,000 simulated US signals, i.e., echoes and jitters backscattered from samples with different ratios of regular-to-irregular structure, as well as with 152 signals obtained from a phantom made of nylon wires. Results: For the simulated signals, the proposed method for estimating the MSS presented results similar to the other methods that were tested. However, the magnitude-of-the-spectrum method loses the phase information, and hence, does not allow the characterization of irregular structures. For the signals recorded from the phantom, the methods using SSA and entropy achieved better results. Conclusion: In this study, the combination of SSA with entropy to estimate the MSS of a periodic or quasi-periodic medium was proposed. The proposed method achieved similar or better results compared with two other methods found in the scientific literature. The novelty of the proposed method is the application of entropy as a quantitative criterion for selecting the SSA periodic components, allowing it to become independent of heuristic criteria.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mean scatterer space estimation from ultrasound signals combining singular spectral analysis and entropy

Kauati¹,

Pereira

Campos

2018

Res. Biomed. Eng.

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“…QUS has been explored for evaluating hepatic steatosis, including speed of sound [11,12], ultrasound attenuation [13,14], backscatter coefficient [14], and shear-wave dispersion [15]. Acoustically, a liver tissue can be thought of as a collection of ultrasound scatterers [16]. The ultrasound backscattered signals are a spatial summation of the scattered waves contributed by each scatterer.…”

Section: Introductionmentioning

confidence: 99%

“…In Section 2, the algorithmic steps for ultrasound envelope statistics parametric imaging will be introduced. Then, statistical model-based and non-model-based ultrasound envelope statistics parametric imaging techniques for hepatic steatosis Acoustically, a liver tissue can be thought of as a collection of ultrasound scatterers [16]. The ultrasound backscattered signals are a spatial summation of the scattered waves contributed by each scatterer.…”

Section: Introductionmentioning

confidence: 99%

Hepatic Steatosis Assessment Using Quantitative Ultrasound Parametric Imaging Based on Backscatter Envelope Statistics

Zhou

Zhang

et al. 2019

Applied Sciences

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Hepatic steatosis is a key manifestation of non-alcoholic fatty liver disease (NAFLD). Early detection of hepatic steatosis is of critical importance. Currently, liver biopsy is the clinical golden standard for hepatic steatosis assessment. However, liver biopsy is invasive and associated with sampling errors. Ultrasound has been recommended as a first-line diagnostic test for the management of NAFLD. However, B-mode ultrasound is qualitative and can be affected by factors including image post-processing parameters. Quantitative ultrasound (QUS) aims to extract quantified acoustic parameters from the ultrasound backscattered signals for ultrasound tissue characterization and can be a complement to conventional B-mode ultrasound. QUS envelope statistics techniques, both statistical model-based and non-model-based, have shown potential for hepatic steatosis characterization. However, a state-of-the-art review of hepatic steatosis assessment using envelope statistics techniques is still lacking. In this paper, envelope statistics-based QUS parametric imaging techniques for characterizing hepatic steatosis are reviewed and discussed. The reviewed ultrasound envelope statistics parametric imaging techniques include acoustic structure quantification imaging, ultrasound Nakagami imaging, homodyned-K imaging, kurtosis imaging, and entropy imaging. Future developments are suggested.

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“…The backscattered radiofrequency (RF) signal is modeled as the interaction of an ultrasound pulse with the scattering structure. The properties of scatterers are related to tissue microstructures (less than wavelength) and can be used to characterize the structural changes that are not obvious in the B-mode ultrasound images [12,13]. Several ultrasound tissue characterization techniques have been introduced in previous studies [14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Classification of Benign and Malignant Breast Tumors Using H-Scan Ultrasound Imaging

Ouyang

Tsui

et al. 2019

Diagnostics

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Breast cancer is one of the most common cancers among women worldwide. Ultrasound imaging has been widely used in the detection and diagnosis of breast tumors. However, due to factors such as limited spatial resolution and speckle noise, classification of benign and malignant breast tumors using conventional B-mode ultrasound still remains a challenging task. H-scan is a new ultrasound technique that images the relative size of acoustic scatterers. However, the feasibility of H-scan ultrasound imaging in the classification of benign and malignant breast tumors has not been investigated. In this paper, we proposed a new method based on H-scan ultrasound imaging to classify benign and malignant breast tumors. Backscattered ultrasound radiofrequency signals of 100 breast tumors were used (48 benign and 52 malignant cases). H-scan ultrasound images were constructed with the radiofrequency signals by matched filtering using Gaussian-weighted Hermite polynomials. Experimental results showed that benign breast tumors had more red components, while malignant breast tumors had more blue components in H-scan ultrasound images. There were significant differences between the RGB channels of H-scan ultrasound images of benign and malignant breast tumors. We conclude H-scan ultrasound imaging can be used as a new method for classifying benign and malignant breast tumors.

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A Review of Ultrasound Tissue Characterization with Mean Scatterer Spacing

Cited by 41 publications

References 83 publications

Mean scatterer space estimation from ultrasound signals combining singular spectral analysis and entropy

Mean scatterer space estimation from ultrasound signals combining singular spectral analysis and entropy

Hepatic Steatosis Assessment Using Quantitative Ultrasound Parametric Imaging Based on Backscatter Envelope Statistics

Classification of Benign and Malignant Breast Tumors Using H-Scan Ultrasound Imaging

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